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Securing an AI system means securing the whole path around its model: the data it receives, the application and integrations that use it, the infrastructure that runs it, and the identities and permissions that authorize actions. Model safeguards matter, but they cannot secure retrieval sources, plugins, credentials, or downstream effects by themselves.
Why AI security extends beyond the model
A model is one component in a larger attack surface. Data ingestion, training, model APIs, monitoring, plugins, storage, orchestration, deployment, and external integrations each introduce different exposures. A review focused only on model behavior can miss weaknesses at the boundaries between these parts.
OWASP recommends beginning with a high-level architecture and refining it to fit the system’s actual data flows, technologies, and integrations. Its threat-modeling guidance emphasizes that “Threats depend on system design” and warns: “Without full architecture visibility, critical attack surfaces can be missed.” OWASP AI Testing Guide: Threat Modeling for AI Systems
What an AI threat model should include
Use data, model, application, and infrastructure as an initial organizing map—not as a final, implementation-level threat model. Draw components and flows, then mark where trust changes or authority is exercised.
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- Data: Identify source systems, ingestion paths, provenance, sensitive information, storage, and the inputs considered trusted.
- Model: Record whether the model is hosted by a provider or self-managed, how it is called, and what information is sent to it.
- Application: Trace prompt construction, orchestration, user-facing behavior, plugins, APIs, and downstream decisions or actions.
- Infrastructure: Include compute, networks, databases, deployment components, monitoring, and relevant dependencies.
- Trust boundaries and authority: Mark external sources, provider boundaries, identities, credentials, permissions, and the points where a system can affect something outside itself.
This map helps connect each component and boundary to relevant threats and countermeasures. It must be specific to the deployment: a broad layer diagram may not capture a hybrid system or dynamically orchestrated workflow. OWASP’s AI threat-modeling guidance
How to secure a RAG application or AI agent
For a RAG application
Trace the complete route from source material to any resulting action. Include how documents enter the system and how their origin is tracked; which users may retrieve which material; the vector store; prompt construction; model calls; generated outputs; and any services or people that act on those outputs. Retrieval and access-control paths deserve attention alongside the model call.
For an agent
Map every tool, plugin, or MCP server the agent can invoke, along with the credentials and delegated permissions behind each one. Record what each tool can read or change and the external effects it can trigger. A change to tools or permissions can alter the system’s risk even if its high-level diagram looks unchanged.
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OWASP’s guidance calls for deployment-specific modeling of RAG and multi-agent designs, including data, retrieval, orchestration, model, vector database, service, and permission paths. Threat Modeling for AI Systems Agent risks also depend on their tools, identities, credentials, and authority. OWASP: Agentic AI Threats and Mitigations
Challenge components and boundaries with threat categories
Use threat categories as prompts for analysis, not as proof that every AI deployment is vulnerable in the same way. Examples in OWASP materials include:
- Prompt injection: Can untrusted content influence instructions or tool use?
- Data poisoning: Could manipulated training or retrieval data affect outputs?
- Model evasion: Could crafted inputs cause the system to behave unexpectedly?
- Privacy breaches: Could sensitive data be exposed through inputs, outputs, logs, or retrieval?
- Rogue actions: Could an agent or application take an unintended action through its tools or permissions?
- Dependency tampering: Could a compromised component in the software or model supply chain affect the system?
OWASP’s materials discuss these as categories to consider; they do not establish a representative rate of architecture failures or a universal ranking of these risks. AI Testing Guide: Threat Modeling for AI Systems OWASP Top 10 for LLM Applications
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Turn the threat model into verifiable controls
For each identified threat, define a control and a way to check whether it works. OWASP’s AI Testing Guide frames mitigations as testable requirements and focuses on post-deployment assessment; it does not cover the full MLOps lifecycle. OWASP AISVS provides AI-specific requirements spanning the AI lifecycle, while expecting teams to check general application, infrastructure, and supply-chain security through parallel standards and practices.
That distinction matters when planning reviews: post-deployment testing is useful but is not a substitute for lifecycle-wide verification, and an AI-specific standard does not replace conventional security work. Use the requirements to shape design reviews, acceptance criteria, CI checks, assessments, and procurement questions. OWASP AI Testing Guide OWASP AI Security Verification Standard (AISVS)
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsRefresh the model when authority or inputs change
Revisit the threat model when the system gains or changes tools, identities, credentials, delegated permissions, trusted inputs, or external effects. These changes can alter what the system is able to access or do, even when the main architecture diagram has not changed. OWASP: Agentic AI Threats and Mitigations
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What OWASP AISVS adds—and what it does not
OWASP says AISVS 1.0 was released in June 2026 and contains 191 requirements across 12 chapters and three appendices. The standard describes its requirements as verifiable, testable, and implementable. That makes it useful for turning security expectations into checks, but AISVS is not presented as an exhaustive replacement for general application, infrastructure, or supply-chain controls. OWASP AI Security Verification Standard
OWASP’s cited official materials provide architecture guidance, threat categories, and security requirements, not a representative statistic for how often AI architecture failures occur. Avoid treating any of the listed threat categories as a measured prevalence rate.
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